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@sysbio-curie

Computational Systems Biology of Cancer at Institut Curie

Computational Systems Biology of Cancer

Our research aims at deciphering the molecular determinants of cancer and make this knowledge available to improve patient management. It is based on high-dimensional and multi-level omics tumor profiles, and proceed by sophisticated machine learning approaches as well as biological network modelling. At Institut Curie our situation is ideal to pursue these goals, since the choice of collaborations with biologists and clinicians, and the many technological core facilities of the institute offer many options to set up original and cutting-edge projects. We are also involved in many national and international (mainly European) projects with other laboratories in Spain, Germany, Italy, Norway, Netherlands, USA or Japan.

website: https://institut-curie.org/team/barillot

Tools

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MaBoSS PhysiBoSS rROMA transmorph
MaBoSS Simulation of continuous/discrete time Markov processes, applied on a Boolean network. PhysiBoSS Multiscale simulation of multi-cellular system rRoma Representation and Quantification of Module Activity from Target Expression Data transmorph Computational framework for dataset integration
   PYPI                    PYPI     
stabilized-ica Neko ElPiGraph NaviCell
stabilized-ica A python implementation of a stabilized ICA algorithm Neko Package to extract, visualize, convert and study interactions from database into executable activity flow based model. ElPiGraph Python implementation of the Elastic Principal Graph algorithm with multi-cpu and gpu support NaviCell A web tool for exploring large maps of molecular interactions
   PYPI           PYPI         

Job Offers

  • PostDoc position on machine learning for multi-modal cancer data analysis

    We are conducting a large-scale study of a lung cancer patient cohort and integrating multimodal data including multiomics (including spatial omics), pathomics, radiomics and other clinical data. Data for ~400 patients have already been collected. The main objective is twofold: build efficient predictors of treatment response and shed light on the mechanisms of tumor progression. The study involves four groups at Institut Curie : Emmanuel Barillot lab (omics), Irène Buvat lab (radiomics), ThomasWalter lab (pathomics) and Nicolas Girard lab (lung oncologist). The first three group leaders also hold chairs (or will soon for IB) at Paris of Artificial Intelligence Research Institute (https://prairie-institute.fr/).

Course materials

From pathway modelling tools to cell-level simulations

This repository includes :

  • The slides of the introduction to MaBoSS, as well as the jupyter notebook presented (link).
  • The files and scripts for the project on the personalisation of cell cycle models in PhysiBoSS. (link).

Pinned

  1. PhysiBoSS PhysiBoSS Public

    Forked from PhysiBoSS/PhysiBoSS

    PhysiBoSS 2.0: a sustainable integration of stochastic Boolean and agent-based modelling frameworks

    Jupyter Notebook 1

  2. MaBoSS MaBoSS Public

    Forked from maboss-bkmc/MaBoSS-env-2.0

    MaBoSS is a C++ software for simulating continuous/discrete time Markov processes, applied on a Boolean network.

    C++ 1 2

  3. NaviCell NaviCell Public

    A web tool for exploring large maps of molecular interactions

    Jupyter Notebook 10 1

  4. rROMA rROMA Public

    An r interface to perform ROMA (updated version 2022)

    HTML 2

  5. stabilized-ica stabilized-ica Public

    Forked from ncaptier/stabilized-ica

    This repository proposes a python implementation of a stabilized ICA algorithm

    Python 1 2

  6. WebMaBoSS WebMaBoSS Public

    Forked from vincent-noel/WebMaBoSS

    A web interface for MaBoSS modeling

    JavaScript 1 1

Repositories

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